SoroushVahidi/scidocs huggingface dataset:
Bartendr: a practical approach to energy-aware cellular data scheduling
(2010)

Cellular radios consume more power and suffer reduced data rate when the signal is weak. According to our measurements, the communication energy per bit can be as much as 6x higher when the signal is weak than when it is strong. To realize energy savings, applications must preferentially communicate when the signal is strong, either by deferring non-urgent communication or by advancing anticipated communication to coincide with periods of strong signal. Allowing applications to perform such scheduling requires predicting signal strength, so that opportunities for energy-efficient communication can be anticipated. Furthermore, such prediction must be performed at little energy cost.
 In this paper, we make several contributions towards a practical system for energy-aware cellular data scheduling called Bartendr. First, we establish, via measurements, the relationship between signal strength and power consumption. Second, we show that location alone is not sufficient to predict signal strength and motivate the use of tracks to enable effective prediction. Finally, we develop energy-aware scheduling algorithms for different workloads - syncing and streaming - and evaluate these via simulation driven by traces obtained during actual drives, demonstrating energy savings of up to 60%. Our experiments have been performed on four cellular networks across two large metropolitan areas, one in India and the other in the U.S.
Accurate online power estimation and automatic battery behavior based power model generation for smartphones
(2010)

This paper describes PowerBooter, an automated power model construction technique that uses built-in battery voltage sensors and knowledge of battery discharge behavior to monitor power consumption while explicitly controlling the power management and activity states of individual components. It requires no external measurement equipment. We also describe PowerTutor, a component power management and activity state introspection based tool that uses the model generated by PowerBooter for online power estimation. PowerBooter is intended to make it quick and easy for application developers and end users to generate power models for new smartphone variants, which each have different power consumption properties and therefore require different power models. PowerTutor is intended to ease the design and selection of power efficient software for embedded systems. Combined, PowerBooter and PowerTutor have the goal of opening power modeling and analysis for more smartphone variants and their users.
Applied spatial data analysis with R
(2008)

Do you need the book of Applied Spatial Data Analysis with R pdf with ISBN of 9781461476177? You will be glad to know that right now Applied Spatial Data Analysis with R pdf is available on our book collections. This Applied Spatial Data Analysis with R comes PDF and EPUB document format. If you want to get Applied Spatial Data Analysis with R pdf eBook copy, you can download the book copy here. The Applied Spatial Data Analysis with R we think have quite excellent writing style that make it easy to comprehend.
Diversity in smartphone usage
(2010)

Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -- interactions with the device and the applications used -- and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users.
Chip to wafer temporary bonding with self-alignment by patterned FDTS layer for size-free MEMS integration
(2011)

In this paper, we present a low-cost and rapid self-alignment process for temporary bonding of MEMS chip onto carrier wafer for size-free MEMS-IC integration. For the first time, a hydrophobic self-assembled monolayer (SAM), FDTS (CF<inf>3</inf>(CF<inf>2</inf>)<inf>7</inf>(CH<inf>2</inf>)<inf>2</inf>SiCl<inf>3</inf>), was successfully patterned by lift-off process on an oxidized silicon carrier wafer. Small volume of H<inf>2</inf>O (∼µl/cm<sup>2</sup>) was then dropped and spread on the non-coated hydrophilic SiO<inf>2</inf> surface for temporary bonding of MEMS chip. Our results demonstrated that the hydrophobic FDTS pattern on carrier wafer enables rapid and precise self-alignment of MEMS chip onto SiO<inf>2</inf> binding-site by capillary force. After transfer the MEMS chips to target wafer, FDTS can be removed by O<inf>2</inf> plasma treatment or UV irradiation.
Exploiting users' social relations to forward data in opportunistic networks: The HiBOp solution
(2008)

Opportunistic networks, in which nodes opportunistically exploit any pair-wise contact to identify next hops towards the destination, are one of the most interesting technologies to support the pervasive networking vision. Opportunistic networks allow content sharing between mobile users without requiring any pre-existing Internet infrastructure, and tolerate partitions, long disconnections, and topology instability in general. In this paperwe propose a context-aware framework for routing and forwarding in opportunistic networks. The framework is general, and able to host various flavors of context-aware routing. In this work we also present a particular protocol, HiBOp, which, by exploiting the framework, learns and represents through context information, the users’ behavior and their social relations, and uses this knowledge to drive the forwarding process. The comparison of HiBOp with reference to alternative solutions shows that a context-aware approach based on users’ social relations turns out to be a very efficient solution for forwarding in opportunistic networks.We showperformance improvements over the reference solutions both in terms of resource utilization and in terms of user perceived QoS. © 2008 Elsevier B.V. All rights reserved.
Are Cyberbullies Less Empathic? Adolescents' Cyberbullying Behavior and Empathic Responsiveness
(2011)

Meta-analyses confirm a negative relationship between aggressive behavior and empathy, that is, the ability to understand and share the feelings of others. Based on theoretical considerations, it was, therefore, hypothesized that a lack of empathic responsiveness may be characteristic for cyberbullies in particular. In the present study, 2.070 students of Luxembourg secondary schools completed an online survey that included a cyberbullying questionnaire(4) and a novel empathy short scale. According to the main hypothesis, analyses of variances indicated that cyberbullies demonstrated less empathic responsiveness than non-cyberbullies. In addition, cyberbullies were also more afraid of becoming victims of cyberbullying. The findings confirm and substantially extend the research on the relationship between empathy and aggressive behavior. From an educational point of view, the present findings suggest that training of empathy skills might be an important tool to decrease cyberbullying.
Applying Graph theory to the Internet of Things
(2013)

In the Internet of Things (IoT), we all are ``things''. Graph theory, a branch of discrete mathematics, has been proven to be useful and powerful in understanding complex networks in history. By means of graph theory, we define new concepts and terminology, and explore the definition of IoT, and then show that IoT is the union of a topological network, a data-functional network and a domi-functional network.
Dermatologists' accuracy in early diagnosis of melanoma of the nail matrix.
(2010)

OBJECTIVE
To measure and compare the accuracy of 4 different clinical methods in the diagnosis of melanoma in situ of the nail matrix among dermatologists with different levels of clinical experience.


DESIGN
Twelve cases of melanonychias (5 melanomas and 7 nonmelanomas) were presented following 4 successive steps: (1) clinical evaluation, (2) evaluation according to the ABCDEF rule, (3) dermoscopy of the nail plate, and (4) intraoperative dermoscopy. At each step, the dermatologists were asked to decide if the lesion was a melanoma.


SETTING
The test was administered at 2 dermatological meetings in 2008.


PARTICIPANTS
A total of 152 dermatologists, including 11 nail experts, 53 senior dermatologists, and 88 junior dermatologists.


MAIN OUTCOME MEASURES
The answers were evaluated as percentage of right answers for each diagnostic step according to the different grade of expertise. Differences among the percentage of right answers in the different steps were evaluated with the z test at a 5% level of significance. The agreement was investigated using Cohen kappa statistic.


RESULTS
The only method that statistically influenced the correct diagnosis for each category (experts, seniors, and juniors) was intraoperative dermoscopy (z test; P < .05). Cohen kappa statistic showed a moderate interobserver agreement.


CONCLUSIONS
Overall accuracy of dermatologists in the diagnosis of nail matrix melanoma in situ is low because the percentages of physicians who indicated the correct diagnosis during each of the first 3 clinical steps of the test ranged from 46% to 55%. The level of expertise did not statistically influence the correct diagnosis.
Rethink fronthaul for soft RAN
(2015)

In this article we discuss the design of a new fronthaul interface for future 5G networks. The major shortcomings of current fronthaul solutions are first analyzed, and then a new fronthaul interface called next-generation fronthaul interface (NGFI) is proposed. The design principles for NGFI are presented, including decoupling the fronthaul bandwidth from the number of antennas, decoupling cell and user equipment processing, and focusing on high-performancegain collaborative technologies. NGFI aims to better support key 5G technologies, in particular cloud RAN, network functions virtualization, and large-scale antenna systems. NGFI claims the advantages of reduced bandwidth as well as improved transmission efficiency by exploiting the tidal wave effect on mobile network traffic. The transmission of NGFI is based on Ethernet to enjoy the benefits of flexibility and reliability. The major impact, challenges, and potential solutions of Ethernet-based fronthaul networks are also analyzed. Jitter, latency, and time and frequency synchronization are the major issues to overcome.
Language within our grasp
(1998)

In monkeys, the rostral part of ventral premotor cortex (area F5) contains neurons that discharge, both when the monkey grasps or manipulates objects and when it observes the experimenter making similar actions. These neurons (mirror neurons) appear to represent a system that matches observed events to similar, internally generated actions, and in this way forms a link between the observer and the actor. Transcranial magnetic stimulation and positron emission tomography (PET) experiments suggest that a mirror system for gesture recognition also exists in humans and includes Broca's area. We propose here that such an observation/execution matching system provides a necessary bridge from'doing' to'communicating',as the link between actor and observer becomes a link between the sender and the receiver of each message.
DCU-UVT: Word-Level Language Classification with Code-Mixed Data
(2014)

This paper describes the DCU-UVT team’s participation in the Language Identification in Code-Switched Data shared task in the Workshop on Computational Approaches to Code Switching. Wordlevel classification experiments were carried out using a simple dictionary-based method, linear kernel support vector machines (SVMs) with and without contextual clues, and a k-nearest neighbour approach. Based on these experiments, we select our SVM-based system with contextual clues as our final system and present results for the Nepali-English and Spanish-English datasets.
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network
(2018)

Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context. In this paper, we investigate matching a response with its multi-turn context using dependency information based entirely on attention. Our solution is inspired by the recently proposed Transformer in machine translation (Vaswani et al., 2017) and we extend the attention mechanism in two ways. First, we construct representations of text segments at different granularities solely with stacked self-attention. Second, we try to extract the truly matched segment pairs with attention across the context and response. We jointly introduce those two kinds of attention in one uniform neural network. Experiments on two large-scale multi-turn response selection tasks show that our proposed model significantly outperforms the state-of-the-art models.
Multi-label Deep Regression and Unordered Pooling for Holistic Interstitial Lung Disease Pattern Detection
(2016)

Holistically detecting interstitial lung disease (ILD) patterns from CT images is challenging yet clinically important. Unfortunately, most existing solutions rely on manually provided regions of interest, limiting their clinical usefulness. In addition, no work has yet focused on predicting more than one ILD from the same CT slice, despite the frequency of such occurrences. To address these limitations, we propose two variations of multi-label deep convolutional neural networks (CNNs). The first uses a deep CNN to detect the presence of multiple ILDs using a regression-based loss function. Our second variant further improves performance, using spatially invariant Fisher Vector encoding of the CNN feature activations. We test our algorithms on a dataset of 533 patients using five-fold cross-validation, achieving high area-under-curve (AUC) scores of 0.982, 0.972, 0.893 and 0.993 for Ground Glass, Reticular, Honeycomb and Emphysema, respectively. As such, our work represents an important step forward in providing clinically effective ILD detection.
Syntax-based Alignment of Multiple Translations: Extracting Paraphrases and Generating New Sentences
(2003)

We describe a syntax-based algorithm that automatically builds Finite State Automata (word lattices) from semantically equivalent translation sets. These FSAs are good representations of paraphrases. They can be used to extract lexical and syntactic paraphrase pairs and to generate new, unseen sentences that express the same meaning as the sentences in the input sets. Our FSAs can also predict the correctness of alternative semantic renderings, which may be used to evaluate the quality of translations.
Narrating a Knowledge Base
(2018)

We aim to automatically generate natural language narratives about an input structured knowledge base (KB). We build our generation framework based on a pointer network which can copy facts from the input KB, and add two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new table position self-attention to capture the inter-dependencies among related slots. For evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we also propose a KB reconstruction based metric by extracting a KB from the generation output and comparing it with the input KB. We also create a new data set which includes 106,216 pairs of structured KBs and their corresponding natural language descriptions for two distinct entity types. Experiments show that our approach significantly outperforms state-of-the-art methods. The reconstructed KB achieves 68.8% 72.6% F-score.1
Robust high-dimensional precision matrix estimation
(2014)

The dependency structure of multivariate data can be analyzed using the covariance matrix Σ. In many fields the precision matrix Σ −1 is even more informative. As the sample covariance estimator is singular in high-dimensions, it cannot be used to obtain a precision matrix estimator. A popular high-dimensional estimator is the graphical lasso, but it lacks robustness. We consider the high-dimensional independent contamination model. Here, even a small percentage of contaminated cells in the data matrix may lead to a high percentage of contaminated rows. Downweighting entire observations, which is done by traditional robust procedures, would then results in a loss of information. In this paper, we formally prove that replacing the sample covariance matrix in the graphical lasso with an elementwise robust covariance matrix leads to an elementwise robust, sparse precision matrix estimator computable in high-dimensions. Examples of such elementwise robust covariance estimators are given. The final precision matrix estimator is positive definite, has a high breakdown point under elementwise contamination and can be computed fast.
The Impact of Clinical Empathy on Patients and Clinicians : Understanding Empathy ' s Side Effects
(2015)

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.
Computer Forensics Field Triage Process Model
(2006)

With the proliferation of digital based evidence, the need for the timely identification, analysis and interpretation of digital evidence is becoming more crucial. In many investigations critical information is required while at the scene or within a short period of time measured in hours as opposed to days. The traditional cyber forensics approach of seizing a system(s)/media, transporting it to the lab, making a forensic image(s), and then searching the entire system for potential evidence, is no longer appropriate in some circumstances. In cases such as child abductions, pedophiles, missing or exploited persons, time is of the essence. In these types of cases, investigators dealing with the suspect or crime scene need investigative leads quickly; in some cases it is the difference between life and death for the victim(s). The Cyber Forensic Field Triage Process Model (CFFTPM) proposes an onsite or field approach for providing the identification, analysis and interpretation of digital evidence in a short time frame, without the requirement of having to take the system(s)/media back to the lab for an in-depth examination or acquiring a complete forensic image(s). The proposed model adheres to commonly held forensic principles, and does not negate the ability that once the initial field triage is concluded, the system(s)/storage media be transported back to a lab environment for a more thorough examination and analysis. The CFFTPM has been successfully used in various real world cases, Journal of Digital Forensics, Security and Law, Vol. 1(2) 20 and its investigative importance and pragmatic approach has been amply demonstrated. Furthermore, the derived evidence from these cases has not been challenged in the court proceedings where it has been introduced. The current article describes the CFFTPM in detail, discusses the model’s forensic soundness, investigative support capabilities and practical considerations.
Saliency-Guided Detection of Unknown Objects in RGB-D Indoor Scenes
(2015)

This paper studies the problem of detecting unknown objects within indoor environments in an active and natural manner. The visual saliency scheme utilizing both color and depth cues is proposed to arouse the interests of the machine system for detecting unknown objects at salient positions in a 3D scene. The 3D points at the salient positions are selected as seed points for generating object hypotheses using the 3D shape. We perform multi-class labeling on a Markov random field (MRF) over the voxels of the 3D scene, combining cues from object hypotheses and 3D shape. The results from MRF are further refined by merging the labeled objects, which are spatially connected and have high correlation between color histograms. Quantitative and qualitative evaluations on two benchmark RGB-D datasets illustrate the advantages of the proposed method. The experiments of object detection and manipulation performed on a mobile manipulator validate its effectiveness and practicability in robotic applications.
Interactive Character Animation Using Simulated Physics: A State-of-the-Art Review
(2012)

Physics simulation offers the possibility of truly responsive and realistic animation. Despite wide adoption of physics simulation for the animation of passive phenomena, such as fluids, cloths and rag-doll characters, commercial applications still resort to kinematics-based approaches for the animation of actively controlled characters. However, following a renewed interest in the use of physics simulation for interactive character animation, many recent publications demonstrate tremendous improvements in robustness, visual quality and usability. We present a structured review of over two decades of research on physics-based character animation, as well as point out various open research areas and possible future directions.
Resilient packet delivery through software defined redundancy: An experimental study
(2017)

Mission-critical applications that use computer networks require guaranteed communication with bounds on the time needed to deliver packets, even if hardware along a path fails or congestion occurs. Current networking technologies follow a reactive approach in which retransmission mechanisms and routing update protocols respond once packet loss occurs. We present a proactive approach that pre-builds and monitors redundant paths to ensure timely delivery with no recovery latency, even in the case of a single-link failure. The paper outlines the approach, describes a new mechanism we call a redundancy controller, and explains the use of SDN to establish and change paths. The paper also reports on a prototype system and gives experimental measurements.